The current spate of very hot weather across much of the USA (and not forgetting the balmy “winter” days in many states back in the early part of the year) raises the question of the role of climate change in relation to such extremes. Of course long term changes in the climate and the occurrence of daily and weekly weather events are very different things, with the latter being the direct result of chaotic movement within the atmosphere. But weather does follow some pattern and over the longer term these patterns can be influenced by a shift in the climate.
A few months back I commented on a paper written by James Hansen which examined this issue in some detail. As illustrated in the figure below, Hansen showed that the distribution of seasonal temperature has indeed shifted, leading to an increase in anomalous events. An important change is the emergence of a category of summertime extremely hot outliers, more than three standard deviations (σ) warmer than the 1951-1980 baseline. This hot extreme, which covered much less than 1% of Earth’s surface in the base period, now typically covers about 10% of the land area. He concluded that extreme heat waves, such as that in Texas and Oklahoma in 2011, Moscow in 2010 and now the US heat wave (which has seen thousands of temperature records topple), were “caused” by global warming, because their likelihood was negligible prior to the recent rapid global warming.
Hansen produced an analysis of temperature data which showed this phenomena was indeed happening. Given the current interest in the issue from friends and colleagues in the USA I thought I would try to see what I could establish by doing my own analysis. Long term US temperature data is readily available for download, so I loaded 110 years (1902-2011) of New York Central Park temperature data into Excel and did some number crunching. I looked at the summer period only (June, July and August) and focused on the maximum daytime temperature in °F. I divided the data into two parts, pre 1950 and post 1950. The respective distribution curves are shown in the chart below. 
There is certainly a shift in the distribution curves, although it is quite small. The median temperature in the pre-1950 data is 82, whereas in the post 1950 data it is 83. But even such a small shift changes the probability of a very hot day. The chart below shows that while the probability of an 80°F day has barely shifted, the chance of a 95°F day has risen from 1.8% to 2.3%, or about a quarter. 
Even such a small shift can have a noticeable impact. A chance of 1.8% means that before 1950 there were about 2 summer days with a maximum temperature of 95°F, but since 1950 there has been an extra day every two years. Another way of looking at this is to examine the incidence of heat waves in New York. In the chart below, I have counted the number of days where that day and the previous three days were 90°F or above. Adding a trend line to the graph shows that over 110 years the number of “heat wave” days has increased from one to over three, or alternatively a single annual four day heat wave in 1900 to at least two per year by 2010 or one lasting more than a week (and there are other combinations as well).
Finally, a more marked change shows up when comparing the period from 1902-1925 with the period 1990-2011. Now the chance of a 95°F day in the summer has about doubled!! The Post 1990 distribution is also starting to show the appearance of very hot days (more than 100°F), which hardly existed prior to 1925.
David,
Are we looking at climate change, or are we looking at UHI effects?
Possibly. My only point here was to show that within the day to day weather which is a chaotic process, there are underlying trends that point to certain externalities in play and that those externalities do impact events such as heat waves. In this case the externality might be the urban heat island. Hansen did the more complete analysis with global data.
I was engaged on work at the University of East Anglia a few years ago in which I was required to explore and utilise applied psychological principles in order to communicate to the general population, as though it were real, the hypothesis that humans are significantly and adversely affecting the climate, and that we should change our ways to minimise or reverse this allegedly catastrophic change in climate. In particular the use of fossil fuels was condemned.
What was extremely apparent at the time is that my colleagues and myself knew, to varying degrees of certainty and detail, that this was entirely a bogus assertion without any basis in reality whatsoever. It was known even then that the data upon which the notion was claimed to be based was at best unreliable and at worst deliberately fabricated in order to promote the hypothesis as reality and the false notion that it was well-supported by evidence to a high degree of confidence. However, any dissent from this viewpoint was invariably countered with coercion to adhere to the alleged consensus view or risk sanctions. The justification for this disruption of normal engineering, scientific and even judicial process was that science was nowadays “post-normal”, that is, that scientists and engineers were required and had a moral duty to distort or fabricate evidence in order to promote the highly contentious agenda of a political pressure groups or terrorist organisations or religious affiliations, rather than adhere to professional standards of engineers and scientists.
The full extent of the scientific component of the fraud were largely unknown to me at the time, and the political and commercial intrigue behind it was at the time was completely unknown. Even after the disclosure of the mathematical details of the fraud some years later, the political and commercial intrigue behind its conception and execution leave me bewildered, as is the audacity of those who continue to maintain to this day the reality of the hypothesis and the exposure of the fraud.
A common ploy we were taught was the mis-representation of climatic data by cherry-picking and date selection. Your choice of New York Central Park could hardly be more representative of the techniques used. First you have chosen one particular place, rather than a global average such as sea temperature. Then the particular site is open to all manner of confounding factors, being a micro-climatologically complex site consisting of a small area of parkland surrounding by an urban environment (UHI effect). This renders difficult the accurate application of “adjustments” to the raw temperature data which are needed to give a representation of the climate over a much wider area. Typically these detailed adjustments are about the same as the figures that you are trying to measure, being a fraction of a degree per century. Next you have taken the temperature from NASA’s “homogenized”data, rather than from the actual temperature recorded at the site. Far from correcting for the Urban Heat Island Effect by raising earlier temperatures, GISS made earlier temperatures even lower, thus introducing an entirely bogus warming of 1.3 degrees per century[1].
In reality, measured Central Park July temperatures got a minute amount cooler over the last 110 years (-0.1 K/cy), but you are presenting a false picture suggesting it got a lot warmer( +1.3 K/cy). You are being economical with the truth.
[1] http://i52.tinypic.com/wrm2pz.jpg, NASA/GISS
The only reason I picked NYC Central Park was that the full dataset was available on the web. In fact James Hansen did the more complete analysis of the shift in distribution in the paper that I pointed to in my post. My only point here was to show that within the day to day weather which is a chaotic process, there are underlying trends that point to certain externalities in play and that those externalities do impact events such as heat waves. In this case the externality might be the urban heat island.
All this logic about extreme weather is flawed. CO2 has the biggest effect during night and in cold dry regions. Therefore, greenhouse gases act to decrease extreme weather. So called heatwawes are just naturaly ocurring phenomenon and I’m eagerly awaiting any statistics showing oposite. I can hardly blame global warming for extremely cold and wet weather in Scotland this year. Manmade UHI has obvously big effect on local weather and temperature measurement in cities. But, this has nothing to do with CO2.
The proof is that surface temperatures of pacific ocean has no trend over many decades or that northern hemisphere is warming much faster than southern one while the CO2 concentration is the same.
It appears that CO2 is not dominant driver of the climate and many studies put climate sensitivity to CO2 at 1degC per doubling CO2 concentration. This can’t cause any problems so IPCC guys have to fudge their numbers to be more alarming. You can hardly find anybody more alarming than Hansen. Perhaps you shouldn’t limit yourself to intergovernmental panel (IPCC) to find the truth.
David,
FYI. http://www.worldclimatereport.com/index.php/2012/07/13/the-heat-was-on-before-urbanization-and-greenhouse-gases/
As David seems to be obsessed with extreme weather (or climate disruption how it is called by alarmists) I’m wondering about his opinion to allegedly false testimony (alarmist) Christopher Field has given to US congress.
http://rogerpielkejr.blogspot.nl/2012/08/ipcc-lead-author-misleads-us-congress.html
Are we possibly looking at the impact of the Clean Air Act and a reduction in smog and particulates in New York City? http://static.ddmcdn.com/gif/empire-state-building-1.jpg
Brian,
It’s quite possible that the shift in Central Park temperatures are related to something other than the general warming trend. After all, this is a very local set of data. But the main reason for doing it was to reproduce the approach adopted by Hansen, rather than attribute the change to any particular cause.
David
Apprecaited this!